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Optimal scale combination selection for inconsistent multi-scale decision tables.

Zhu Yingjie1, Yang Bin1

  • 1College of Science, Northwest A & F University, Yangling, 712100 People's Republic of China.

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|May 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces new models for selecting optimal scale combinations from inconsistent multi-scale decision tables. These models extend existing methods, offering a novel approach for data mining tasks involving complex hierarchical data.

Keywords:
Inconsistent multi-scale decision tableMulti-scale decision tableOptimal scale combinationPositive region consistentRough set

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Area of Science:

  • Data Mining
  • Decision Analysis
  • Information Science

Background:

  • Hierarchical structured data are prevalent in real-world applications, necessitating efficient data mining techniques.
  • Selecting optimal scale combinations from multi-scale decision tables is crucial for subsequent data analysis tasks.
  • Existing models for optimal scale selection primarily address consistent multi-scale decision tables, leaving a gap for inconsistent ones.

Purpose of the Study:

  • To introduce and adapt complement and lattice models for inconsistent multi-scale decision tables.
  • To develop algorithms for these new models based on positive region consistency.
  • To provide a theoretical framework for optimal scale combination selection in inconsistent decision tables.

Main Methods:

  • Introduction of complement and lattice models based on Li and Hu's concepts.
  • Development of algorithms for complement and lattice models utilizing positive region consistency.
  • Empirical validation through numerical experiments to assess model performance.

Main Results:

  • The proposed models effectively process inconsistent multi-scale decision tables, exhibiting properties similar to existing models for consistent tables.
  • Application of the positive region consistent models to consistent tables yields comparable results to traditional methods.
  • The lattice model based on positive region consistency demonstrated increased time and resource consumption.

Conclusions:

  • The developed models offer a new theoretical method for optimal scale combination selection from inconsistent multi-scale decision tables.
  • The approach extends the applicability of complement and lattice models to a broader range of decision table types.
  • Further research may explore optimizations for the proposed lattice model to mitigate computational costs.